

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__snake_case				:      Optional[int]   		=			logging.getLogger(__name__)



class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None    )     -> Tuple:
									a_						:						str 							= self.layer[current_layer](_lowercase  ,      _lowercase  ,      head_mask[current_layer]    )

									a_						:						str 							= layer_outputs[0]

									return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> List[str]:
									super().__init__(_lowercase    )

									a_						:						Tuple 							= BertEncoderWithPabee(_lowercase    )

									self.init_weights()
									a_						:						int 							= 0
									a_						:						Any 							= 0
									a_						:						Tuple 							= 0

									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Tuple:
									a_						:						Dict 							= threshold





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[Any]:
									a_						:						Optional[int] 							= patience





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						str 							= 0
									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= self.inference_layers_num / self.inference_instances_num
									a_						:						Optional[int] 							= (
									    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
									    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
									)
									print(_lowercase    )





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      )     -> str:

									if input_ids is not None and inputs_embeds is not None:
												raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""    )
									elif input_ids is not None:
												a_						:						Dict 							= input_ids.size()
									elif inputs_embeds is not None:
												a_						:						Dict 							= inputs_embeds.size()[:-1]
									else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

									a_						:						Optional[Any] 							= input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
												a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
									if token_type_ids is None:
												a_						:						List[str] 							= torch.zeros(_lowercase  ,      dtype=torch.long  ,      device=_lowercase    )

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									a_						:						torch.Tensor 							= self.get_extended_attention_mask(_lowercase  ,      _lowercase  ,      _lowercase    )

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
												a_							,					a_							,					a_						:						int 							= encoder_hidden_states.size()
												a_						:						List[str] 							= (encoder_batch_size, encoder_sequence_length)
												if encoder_attention_mask is None:
															a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
												a_						:						List[Any] 							= self.invert_attention_mask(_lowercase    )
									else:
												a_						:						Optional[Any] 							= None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									a_						:						List[Any] 							= self.get_head_mask(_lowercase  ,      self.config.num_hidden_layers    )

									a_						:						List[str] 							= self.embeddings(
									    input_ids=_lowercase  ,      position_ids=_lowercase  ,      token_type_ids=_lowercase  ,      inputs_embeds=_lowercase    )
									a_						:						List[Any] 							= embedding_output

									if self.training:
												a_						:						Any 							= []
												for i in range(self.config.num_hidden_layers    ):
															a_						:						int 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						List[Any] 							= self.pooler(_lowercase    )
															a_						:						Optional[int] 							= output_layers[i](output_dropout(_lowercase    )    )
															res.append(_lowercase    )
									elif self.patience == 0:  # Use all layers for inference
												a_						:						Union[str, Any] 							= self.encoder(
												    _lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
												a_						:						Union[str, Any] 							= self.pooler(encoder_outputs[0]    )
												a_						:						List[str] 							= [output_layers[self.config.num_hidden_layers - 1](_lowercase    )]
									else:
												a_						:						Any 							= 0
												a_						:						Dict 							= None
												a_						:						Tuple 							= 0
												for i in range(self.config.num_hidden_layers    ):
															calculated_layer_num += 1
															a_						:						Optional[Any] 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						Optional[int] 							= self.pooler(_lowercase    )
															a_						:						int 							= output_layers[i](_lowercase    )
															if regression:
																		a_						:						Dict 							= logits.detach()
																		if patient_result is not None:
																					a_						:						Optional[Any] 							= patient_result.detach()
																		if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
																					patient_counter += 1
																		else:
																					a_						:						Optional[Any] 							= 0
															else:
																		a_						:						str 							= logits.detach().argmax(dim=1    )
																		if patient_result is not None:
																					a_						:						str 							= patient_result.detach().argmax(dim=1    )
																		if (patient_result is not None) and torch.all(labels.eq(_lowercase    )    ):
																					patient_counter += 1
																		else:
																					a_						:						Tuple 							= 0

															a_						:						Union[str, Any] 							= logits
															if patient_counter == self.patience:
																		break
												a_						:						str 							= [patient_result]
												self.inference_layers_num += calculated_layer_num
												self.inference_instances_num += 1

									return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> str:
									super().__init__(_lowercase    )
									a_						:						str 							= config.num_labels

									a_						:						Optional[Any] 							= BertModelWithPabee(_lowercase    )
									a_						:						int 							= nn.Dropout(config.hidden_dropout_prob    )
									a_						:						str 							= nn.ModuleList(
									    [nn.Linear(config.hidden_size  ,      self.config.num_labels    ) for _ in range(config.num_hidden_layers    )]    )

									self.init_weights()





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Optional[Any] 							= self.bert(
									    input_ids=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      position_ids=_lowercase  ,      head_mask=_lowercase  ,      inputs_embeds=_lowercase  ,      output_dropout=self.dropout  ,      output_layers=self.classifiers  ,      regression=self.num_labels == 1  ,      )

									a_						:						Optional[Any] 							= (logits[-1],)

									if labels is not None:
												a_						:						int 							= None
												a_						:						Union[str, Any] 							= 0
												for ix, logits_item in enumerate(_lowercase    ):
															if self.num_labels == 1:
																		#  We are doing regression
																		a_						:						Any 							= MSELoss()
																		a_						:						Union[str, Any] 							= loss_fct(logits_item.view(-1    )  ,      labels.view(-1    )    )
															else:
																		a_						:						Any 							= CrossEntropyLoss()
																		a_						:						int 							= loss_fct(logits_item.view(-1  ,      self.num_labels    )  ,      labels.view(-1    )    )
															if total_loss is None:
																		a_						:						str 							= loss
															else:
																		total_loss += loss * (ix + 1)
															total_weights += ix + 1
												a_						:						Any 							= (total_loss / total_weights,) + outputs

									return outputs





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=99  ,      _lowercase=13  ,      _lowercase=16  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=2  ,      _lowercase=32  ,      _lowercase=4  ,      _lowercase=4  ,      _lowercase=30  ,      _lowercase=0  ,      _lowercase=1  ,      _lowercase=2  ,      _lowercase=None  ,      )     -> List[str]:
									a_						:						Union[str, Any] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						Any 							= decoder_seq_length
									# For common tests
									a_						:						Any 							= self.decoder_seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_attention_mask
									a_						:						List[str] 							= use_labels

									a_						:						Optional[Any] 							= vocab_size
									a_						:						Dict 							= d_model
									a_						:						Any 							= d_model
									a_						:						List[Any] 							= decoder_layers
									a_						:						List[str] 							= decoder_layers
									a_						:						Union[str, Any] 							= decoder_ffn_dim
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Tuple 							= eos_token_id
									a_						:						Union[str, Any] 							= bos_token_id
									a_						:						Dict 							= pad_token_id
									a_						:						Union[str, Any] 							= decoder_start_token_id
									a_						:						List[Any] 							= use_cache
									a_						:						List[str] 							= max_position_embeddings

									a_						:						int 							= None
									a_						:						Any 							= decoder_seq_length
									a_						:						Union[str, Any] 							= 2
									a_						:						Optional[int] 							= 1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Optional[int] 							= None
									if self.use_attention_mask:
												a_						:						List[Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      vocab_size=2    )

									a_						:						List[Any] 							= None
									if self.use_labels:
												a_						:						Union[str, Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Any 							= TrOCRConfig(
									    vocab_size=self.vocab_size  ,      d_model=self.d_model  ,      decoder_layers=self.decoder_layers  ,      decoder_ffn_dim=self.decoder_ffn_dim  ,      decoder_attention_heads=self.decoder_attention_heads  ,      eos_token_id=self.eos_token_id  ,      bos_token_id=self.bos_token_id  ,      use_cache=self.use_cache  ,      pad_token_id=self.pad_token_id  ,      decoder_start_token_id=self.decoder_start_token_id  ,      max_position_embeddings=self.max_position_embeddings  ,      )

									return (config, input_ids, attention_mask, lm_labels)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[Any]:
									a_						:						Optional[int] 							= True
									a_						:						List[str] 							= TrOCRDecoder(config=_lowercase    ).to(_lowercase    ).eval()
									a_						:						str 							= input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									a_						:						Dict 							= model(_lowercase  ,      use_cache=_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase  ,      use_cache=_lowercase    )

									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    )    )
									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    ) + 1    )

									a_						:						Dict 							= outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									a_						:						List[str] 							= ids_tensor((2, 1)  ,      config.vocab_size - 1    ) + 1

									# append to next input_ids and
									a_						:						int 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )

									a_						:						Dict 							= model(_lowercase    )["""last_hidden_state"""]
									a_						:						List[str] 							= model(_lowercase  ,      past_key_values=_lowercase    )["""last_hidden_state"""]

									# select random slice
									a_						:						Optional[Any] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Union[str, Any] 							= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									a_						:						Any 							= output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						List[Any] 							= config_and_inputs

									a_						:						str 							= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(TrOCRForCausalLM,) if is_torch_available() else ()
						_A    :				Tuple       					=			{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
						_A    :				List[str]       					=			True
						_A    :				List[str]       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						str 							= TrOCRStandaloneDecoderModelTester(self  ,      is_training=_lowercase    )
									a_						:						str 							= ConfigTester(self  ,      config_class=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						def 			UpperCamelCase__					(    self    )     -> int:
									pass





						def 			UpperCamelCase__					(    self    )     -> str:
									pass





						def 			UpperCamelCase__					(    self    )     -> Dict:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return





						@unittest.skip("""The model doesn't support left padding"""    )  # and it's not used enough to be worth fixing :)
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass


